Query optimization has played a central role in database research for decades. However, more often than not, the proposed optimization techniques lead to a performance improvement in some, but not in all, situations. Therefore, we urgently need a methodology for designing a decision procedure that decides for a given query whether the optimization technique should be applied or not.In this work, we propose such a methodology with a focus on Yannakakis-style query evaluation as our optimization technique of interest. More specifically, we formulate this decision problem as an algorithm selection problem and we present a Machine Learning based approach for its solution. Empirical results with several benchmarks on a variety of database systems show that our approach indeed leads to a statistically significant performance improvement.
View on arXiv@article{böhm2025_2502.20233,
title={ Selective Use of Yannakakis' Algorithm to Improve Query Performance: Machine Learning to the Rescue },
author={ Daniela Böhm and Georg Gottlob and Matthias Lanzinger and Davide Longo and Cem Okulmus and Reinhard Pichler and Alexander Selzer },
journal={arXiv preprint arXiv:2502.20233},
year={ 2025 }
}